Brave Induction: a logical framework for learning from incomplete information

Chiaki Sakama and Katsumi Inoue

Machine Learning, 76:3-35, Springer-Verlag, 2009.

Abstract

This paper introduces a novel logical framework for concept-learning called brave induction.
Brave induction uses brave inference for induction and is useful for learning from incomplete information.
Brave induction is weaker than explanatory induction which is normally used in inductive logic programming,
and is stronger than learning from satisfiability,
a general setting of concept-learning in clausal logic.
We first investigate formal properties of brave induction, then
develop an algorithm for computing hypotheses in full clausal theories.
Next we extend the framework to induction in nonmonotonic logic programs.
We analyze computational complexity of decision problems for induction on propositional theories.
Further, we provide problem solving by brave induction
in systems biology, requirement engineering, and multiagent negotiation.